code: def count_parameters(model):
      print("Modules, Parameters")
      total_params = 0
      for name, parameter in model.named_parameters():
          if not parameter.requires_grad:
              continue
          param = parameter.numel()
          # table.add_row([name, param])
          total_params += param
          print(name, param)
      print(f"Total Trainable Params: {total_params}")
      return total_params
  
file_path: EasyOCR/trainer/train.py
imports: import os
  import sys
  import time
  import random
  import torch
  import torch.backends.cudnn
  import torch.nn
  import torch.nn.init
  import torch.optim
  import torch.utils.data
  from torch.cuda.amp import autocast, GradScaler
  import numpy
  from utils import CTCLabelConverter, AttnLabelConverter, Averager
  from dataset import hierarchical_dataset, AlignCollate, Batch_Balanced_Dataset
  from model import Model
  from test import validation
label_desc: Use image node_id 1 for calling a global function with example usage: count_parameters(model) and returns: total_params